datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
tulu-2.5-preference-data
Tulu 2.5 Preference Data
This dataset contains the preference dataset splits used to train the models described in Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback.
We cleaned and formatted all datasets to be in the same format.
This means some splits may differ from their original format.
To see the code used for creating most splits, see here.
If you only wish to download one dataset, each dataset exists in one file under the data/… See the full description on the dataset page: https://huggingface.co/datasets/allenai/tulu-2.5-preference-data.700k_Human_Preference_Dataset_FLUX_SD3_MJ_DALLE3
NOTE: A newer version of this dataset is available Imagen3_Flux1.1_Flux1_SD3_MJ_Dalle_Human_Preference_Dataset
Rapidata Image Generation Preference Dataset
This Dataset is a 1/3 of a 2M+ human annotation dataset that was split into three modalities: Preference, Coherence, Text-to-Image Alignment.
Link to the Coherence dataset: https://huggingface.co/datasets/Rapidata/Flux_SD3_MJ_Dalle_Human_Coherence_Dataset
Link to the Text-2-Image Alignment dataset:… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/700k_Human_Preference_Dataset_FLUX_SD3_MJ_DALLE3.preference-datasets-tulupreference_data_llama_factory_wo_checklist
Dataset Card for "preference_data_llama_factory_wo_checklist"
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Apertus-v1.5-Preference-Data
Apertus 1.5 Preference Dataset
This is the preference dataset used for the offline DPO stage of Apertus v1.5 alignment training, applied to the 70B model.
The prompts come from Ai2's Olmo 3 Dolci-Instruct-DPO dataset. We only reuse the prompts from Dolci-Instruct-DPO; all chosen / rejected responses in this dataset were generated by us.
How this dataset was built
Prompts. Taken from Dolci-Instruct-DPO (ODC-BY).
Response generation and annotation. Every prompt was… See the full description on the dataset page: https://huggingface.co/datasets/swiss-ai/Apertus-v1.5-Preference-Data.preference_dataset_mixture2_and_safe_pku
Copy from https://huggingface.co/datasets/weqweasdas/preference_dataset_mixture2_and_safe_pku
Reward Model Overview
This is the data mixture used for the reward model weqweasdas/RM-Mistral-7B, trained with the script https://github.com/WeiXiongUST/RLHF-Reward-Modeling .
Also see a short blog for the training details (data mixture, parameters...): https://www.notion.so/Reward-Modeling-for-RLHF-abe03f9afdac42b9a5bee746844518d0
Model Details
If you have any question… See the full description on the dataset page: https://huggingface.co/datasets/OpenRLHF/preference_dataset_mixture2_and_safe_pku.pair_preference_model_dataset_add_emoji_to_win_rate0.1_rrm_newpair_preference_model_dataset_gemma2_2b_rrm_0p2pair_preference_model_dataset_add_prefix_to_win_rate0.1_rrm_0p21.4b-policy_preference_data_gold_labelledPreference dataset using labels from the AlpacaFarm dataset, generated answers from a 1.4b fine-tuned Pythia policy model, and labelled using the AlpacaFarm 'reward-model-human' as a gold reward model.
Used to train reward models in 'Reward Model Ensembles Mitigate Overoptimization'
pair_preference_model_dataset_bold_face_to_win_rate0.1_rrm_newpair_preference_model_dataset_add_prefix_to_win_rate0.1_rrmdataset-viber-image-generation-preference-inference-endpoints-battle-flux
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Dataset Details
Dataset Description
Curated by: [More Information Needed]
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Language(s) (NLP): [More Information Needed]
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Demo [optional]: [More Information Needed]… See the full description on the dataset page: https://huggingface.co/datasets/davidberenstein1957/dataset-viber-image-generation-preference-inference-endpoints-battle-flux.pair-preference-dataset-700K_standardproactivity_preference_dataset
ProVoice study 1 — driver state, vehicle context and preferred Level of Autonomy
Driving-simulator data from the population data collection of the ProVoice /
ProActivity project (CARLA 0.10): 12 drivers × 2 sessions, ~20 Hz
multimodal driver-state and vehicle frames, and 1,446 driver-assigned
Level-of-Autonomy (LoA) labels stating how autonomously an in-vehicle
assistant should act on a given task. Drivers were prompted every 20 s about
two randomly drawn in-vehicle tasks and… See the full description on the dataset page: https://huggingface.co/datasets/ProVoice-proactivity/proactivity_preference_dataset.preference_data_llama_factory_corrected_format_text_onlyPRO-STEP-Preference-Data
PRO-STEP: DPO Preference Pairs
Step-level preference pairs used to train the PRO-STEP policy model via Direct Preference Optimization.
Paper: PRO-STEP: Step-level Process Reward Optimization for Retrieval-Augmented GenerationCode: GitHub Repository
Pairs: 15,877 (after outcome filter)
Source questions: 5,000 from HotpotQA + MuSiQue + 2WikiMultiHopQA training splits
Generation: PRM-guided MCTS (K=3 branching, depth 7, 64 rollouts/question, V(s) = Q̄(s) + α · r̂(s) with α=0.3)… See the full description on the dataset page: https://huggingface.co/datasets/MinKeonKim/PRO-STEP-Preference-Data.preference_dataset-standard_format-v2.2gemma-3-27b-it-antislop-ftpo-preference-datasetpreference_data_llama_factory_corrected_formatpreference_dataset_mixture2_and_safe_pku
Reward Model Overview
This is the data mixture used for the reward model weqweasdas/RM-Mistral-7B, trained with the script https://github.com/WeiXiongUST/RLHF-Reward-Modeling .
Also see a short blog for the training details (data mixture, parameters...): https://www.notion.so/Reward-Modeling-for-RLHF-abe03f9afdac42b9a5bee746844518d0
Model Details
If you have any question with this reward model and also any question about reward modeling, feel free to drop me an… See the full description on the dataset page: https://huggingface.co/datasets/weqweasdas/preference_dataset_mixture2_and_safe_pku.Preference_Dataset_Merged
Dataset Overview
This Dataset consists of the following open-sourced preference dataset
Arena Human Preference
Anthropic HH
MT-Bench Human Judgement
Ultra Feedback
Tulu3 Preference Dataset
Skywork-Reward-Preference-80K-v0.2
Cleaning
Cleaning Method 1: Only keep the following Language using FastText language detection(EN/DE/ES/ZH/IT/JA/FR)
Cleaning Method 2: Remove duplicates to ensure each prompt appears only once
Cleaning Method 3: Remove datasets where… See the full description on the dataset page: https://huggingface.co/datasets/yufan/Preference_Dataset_Merged.1.4b-policy_preference_data_armorm_gold_labelledtulu-2_5-preference-data-sharegptuniversal-preference-hijacking-datasets
Phi: Preference Hijacking in Multi-modal Large Language Models at Inference Time
Figure 1: Examples of Phi, which can hijack MLLM's preference toward the image.
Figure 2: Example of a universal hijacking perturbation, which can be transferred across different images.
This dataset is used to train and evaluate the universal hijacking perturbations in the paper "Phi: Preference Hijacking in Multi-modal Large Language Models at Inference Time", accepted at EMNLP… See the full description on the dataset page: https://huggingface.co/datasets/yflantmy/universal-preference-hijacking-datasets.pair-preference-dataset-700K_subset-15-out-of-16_standardpreference_dataset_mix2
Dataset Card for "preference_dataset_mix2"
More Information needed
Synthetic-JP-Preference-Dataset-Qwen2.5_72B-191k
Synthetic-JP-Preference-Dataset-Qwen2.5_72B-191k
概要
5種類のオープンモデルとQwen/Qwen2.5-72B-Instruct-GPTQ-Int8を使って作成した、190854件の日本語合成Preferenceデータセットです。
以下、データセットの詳細です。
instructionには、Aratako/Magpie-Tanuki-8B-annotated-96kのinput_qualityがexcellentのものを利用
回答生成には、以下の5つのApache 2.0ライセンスのモデルを利用
weblab-GENIAC/Tanuki-8B-dpo-v1.0
team-hatakeyama-phase2/Tanuki-8x8B-dpo-v1.0-GPTQ-8bit
cyberagent/calm3-22b-chat
llm-jp/llm-jp-3-13b-instruct
Qwen/Qwen2.5-32B-Instruct-GPTQ-Int8… See the full description on the dataset page: https://huggingface.co/datasets/Aratako/Synthetic-JP-Preference-Dataset-Qwen2.5_72B-191k.POVID_preference_data_for_VLLMsstackexchange-preference-data